Applying machine learning to decode built environment thresholds for public and active transport distances in the global south
Urban mobility in rapidly growing megacities, particularly in the Global South, presents unique challenges due to population densities, fragmented transit networks, and informal urban growth. While extensive research has examined how the built environment (BE) influences transport mode choice, the i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Journal of Urban Mobility |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667091725000457 |
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| author | Ali Shkera Domokos Esztergár-Kiss |
| author_facet | Ali Shkera Domokos Esztergár-Kiss |
| author_sort | Ali Shkera |
| collection | DOAJ |
| description | Urban mobility in rapidly growing megacities, particularly in the Global South, presents unique challenges due to population densities, fragmented transit networks, and informal urban growth. While extensive research has examined how the built environment (BE) influences transport mode choice, the impact of BE on active transport (AT) and public transport (PT) trip distances remains partially underexplored, particularly in India. The current study addresses this gap by analyzing the non-linear effects of BE characteristics on trip distances for both AT and PT in the Mumbai Metropolitan Region. By using gradient boosting decision trees, alongside six other machine learning (ML) models, including support vector machines, random forest, and artificial neural networks, this study identifies key BE factors that shape trip distances. The findings reveal that BE variables account for 66 % of the variance in AT distance and 63 % in PT distance. This highlights the dominant role of urban form over socio-demographic factors. Notably, proximity to railway stations, land-use diversity, and intersection density exhibit strong threshold effects on travel distances. Additionally, partial dependence plots uncover non-linear BE-travel behavior interactions demonstrating that moderate densities (300–500 blocks/km²) optimize AT, while PT ridership is more sensitive to network accessibility. The study provides data-driven policy recommendations to enhance pedestrian infrastructure, refine transit-oriented development, and promote sustainable multimodal mobility. By integrating advanced ML methods with transportation policy analysis, this research bridges critical methodological and contextual gaps thus offering actionable insights for urban planners in high-density, transit-dependent cities worldwide. |
| format | Article |
| id | doaj-art-bf1e4ec2196a4da1bdbdad920ee85dcb |
| institution | Kabale University |
| issn | 2667-0917 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Urban Mobility |
| spelling | doaj-art-bf1e4ec2196a4da1bdbdad920ee85dcb2025-08-20T03:59:35ZengElsevierJournal of Urban Mobility2667-09172025-12-01810014310.1016/j.urbmob.2025.100143Applying machine learning to decode built environment thresholds for public and active transport distances in the global southAli Shkera0Domokos Esztergár-Kiss1Department of Civil Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, IndiaBudapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Department of Transport Technology and Economics, Műegyetem rkp. 3, Budapest 1111, Hungary; Corresponding author.Urban mobility in rapidly growing megacities, particularly in the Global South, presents unique challenges due to population densities, fragmented transit networks, and informal urban growth. While extensive research has examined how the built environment (BE) influences transport mode choice, the impact of BE on active transport (AT) and public transport (PT) trip distances remains partially underexplored, particularly in India. The current study addresses this gap by analyzing the non-linear effects of BE characteristics on trip distances for both AT and PT in the Mumbai Metropolitan Region. By using gradient boosting decision trees, alongside six other machine learning (ML) models, including support vector machines, random forest, and artificial neural networks, this study identifies key BE factors that shape trip distances. The findings reveal that BE variables account for 66 % of the variance in AT distance and 63 % in PT distance. This highlights the dominant role of urban form over socio-demographic factors. Notably, proximity to railway stations, land-use diversity, and intersection density exhibit strong threshold effects on travel distances. Additionally, partial dependence plots uncover non-linear BE-travel behavior interactions demonstrating that moderate densities (300–500 blocks/km²) optimize AT, while PT ridership is more sensitive to network accessibility. The study provides data-driven policy recommendations to enhance pedestrian infrastructure, refine transit-oriented development, and promote sustainable multimodal mobility. By integrating advanced ML methods with transportation policy analysis, this research bridges critical methodological and contextual gaps thus offering actionable insights for urban planners in high-density, transit-dependent cities worldwide.http://www.sciencedirect.com/science/article/pii/S2667091725000457Global southNon-linear relationshipsMachine learningBuilt environmentUrban mobility |
| spellingShingle | Ali Shkera Domokos Esztergár-Kiss Applying machine learning to decode built environment thresholds for public and active transport distances in the global south Journal of Urban Mobility Global south Non-linear relationships Machine learning Built environment Urban mobility |
| title | Applying machine learning to decode built environment thresholds for public and active transport distances in the global south |
| title_full | Applying machine learning to decode built environment thresholds for public and active transport distances in the global south |
| title_fullStr | Applying machine learning to decode built environment thresholds for public and active transport distances in the global south |
| title_full_unstemmed | Applying machine learning to decode built environment thresholds for public and active transport distances in the global south |
| title_short | Applying machine learning to decode built environment thresholds for public and active transport distances in the global south |
| title_sort | applying machine learning to decode built environment thresholds for public and active transport distances in the global south |
| topic | Global south Non-linear relationships Machine learning Built environment Urban mobility |
| url | http://www.sciencedirect.com/science/article/pii/S2667091725000457 |
| work_keys_str_mv | AT alishkera applyingmachinelearningtodecodebuiltenvironmentthresholdsforpublicandactivetransportdistancesintheglobalsouth AT domokosesztergarkiss applyingmachinelearningtodecodebuiltenvironmentthresholdsforpublicandactivetransportdistancesintheglobalsouth |